Cerebral malaria (CM) is a life-threatening neurological complication associated with malarial infection. Malaria affects about 198 million people worldwide, and claims 584,000 lives annually, 75% of whom are African children under 5 years of age. CM is mistakenly over- diagnosed about 23% of the time, in which the cases of concurrent non-CM diseases with similar symptoms produce false positive test results and result in incorrect treatment. An accurate means to confirm the presence of CM or to investigate for a non-malarial illness is critically needed to improve outcomes. The retinal lesions associated with malarial retinopathy (MR) are highly specific to CM, and a retinal screening for MR represents an effective means to assist in and improve the specificity of CM diagnosis. VisionQuest Biomedical and its collaborators have assembled a team of inter-disciplinary scientists with considerable experience in automated retinal image analysis, clinical ophthalmology with specialized research in malarial retinopathy (MR), and cerebral malaria diagnosis (CM). This team will develop and test ASPIRE, a system for detection of MR consisting of automated MR detection software integrated with a low-cost and portable retinal camera especially designed for use in clinical settings in Africa. The system components will be selected to demonstrate an economically viable product affordable to the targeted population in Africa. The proposed ASPIRE system will augment, not replace, the current CM diagnostic standard; increasing the accuracy of CM diagnoses, leading to a smaller number of false positive outcomes. In Phase I, the research team at VisionQuest Biomedical demonstrated the feasibility of a fully automated MR detection system that includes software to provide automated detection of MR lesions, interfaced with a portable retinal imaging camera. In Phase II, the ASPIRE system will be refined, optimized, and validated on retrospective as well as prospective datasets. We will accomplish this through three specific aims. In the first aim, the software system for MR detection will be optimized to perform in real time and validated on a retrospective image dataset. In the second aim, we will refine the retinal imaging hardware and integrate the software system with the camera, which automatically detects MR. The third aim will focus on evaluating the on-site performance of ASPIRE system in a clinical research study to be conducted in in Malawi, Africa.